size <- 12. Later this will be the number of rows of the matrix.x <- rnorm( size ).x1 by adding (on average 10 times smaller) noise to x: x1 <- x + rnorm( size )/10.x and x1 should be close to 1.0: check this with function cor.x2 and x3 by adding (other) noise to x.size <- 12
x <- rnorm( size )
x1 <- x + rnorm( size )/10
cor( x, x1 )
[1] 0.9945626
x2 <- x + rnorm( size )/10
x3 <- x + rnorm( size )/10
x1, x2 and x3 column-wise into a matrix using m <- cbind( x1, x2, x3 ).m.m.heatmap( m, Colv = NA, Rowv = NA, scale = "none" ).m <- cbind( x1, x2, x3 )
class( m )
[1] "matrix"
head( m )
x1 x2 x3
[1,] 1.51389746 1.5155970 1.50477891
[2,] 1.40798870 1.3655355 1.59466327
[3,] -0.95050756 -1.0012282 -0.89029233
[4,] 0.13175349 0.1478531 0.02385004
[5,] -0.72146922 -0.6779397 -0.65452518
[6,] 0.09893675 -0.1702900 0.06815393
heatmap( m, Colv = NA, Rowv = NA, scale = "none" ) # high is dark red, low is yellow
# x1, x2, x3 follow similar color pattern, they should be correlated
y1…y4 (but not correlated with x), of the same length size.m from columns x1…x3,y1…y4 in some random order.y <- rnorm( size )
y1 <- y + rnorm( size )/10
y2 <- y + rnorm( size )/10
y3 <- y + rnorm( size )/10
y4 <- y + rnorm( size )/10
m <- cbind( y4, y3, x2, y1, x1, x3, y2 )
heatmap( m, Colv = NA, Rowv = NA, scale = "none" ) # high is dark red, low is yellow
cc <- cor( m ) to build the matrix of correlation coefficients between columns of m.round( cc, 3 ) to show this matrix with 3 digits precision.cc <- cor( m )
round( cc, 3 ) #
y4 y3 x2 y1 x1 x3 y2
y4 1.000 0.979 -0.187 0.967 -0.186 -0.239 0.973
y3 0.979 1.000 -0.143 0.970 -0.132 -0.189 0.972
x2 -0.187 -0.143 1.000 -0.163 0.994 0.991 -0.211
y1 0.967 0.970 -0.163 1.000 -0.158 -0.216 0.981
x1 -0.186 -0.132 0.994 -0.158 1.000 0.994 -0.210
x3 -0.239 -0.189 0.991 -0.216 0.994 1.000 -0.258
y2 0.973 0.972 -0.211 0.981 -0.210 -0.258 1.000
heatmap( cc, symm = TRUE, scale = "none" )
# E.g. value for (row: x1, col: y1) is the corerlation of vectors x1, y1.
# Values of 1.0 are on the diagonal: e.g. x1 is perfectly correlated with x1.
# Correlations between x, x vectors are close to 1.0.
# Correlations between y, y vectors are close to 1.0.
# Correlations between x, y vectors are close to 0.0.
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